RUIF013 updates and formatting

modules/prompt_parser_diffusers.py

@@ -753,7 +753,7 @@ def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", c
     return prompt_embeds, pooled_prompt_embeds, None, negative_prompt_embeds, negative_pooled_prompt_embeds, None
 
 
-def get_xhinker_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", clip_skip: int = None):
+def get_xhinker_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", clip_skip: int | None = None):
     is_sd3 = hasattr(pipe, 'text_encoder_3')
     prompt, prompt_2, _prompt_3, _ = split_prompts(pipe, prompt, is_sd3)
     neg_prompt, neg_prompt_2, _neg_prompt_3, _ = split_prompts(pipe, neg_prompt, is_sd3)

modules/prompt_parser_xhinker.py

@@ -27,10 +27,7 @@ from diffusers import ChromaPipeline
 from modules.prompt_parser import parse_prompt_attention  # use built-in A1111 parser
 
 
-def get_prompts_tokens_with_weights(
-        clip_tokenizer: CLIPTokenizer
-        , prompt: str = None
-):
+def get_prompts_tokens_with_weights(clip_tokenizer: CLIPTokenizer, prompt: str | None = None):
     """
     Get prompt token ids and weights, this function works for both prompt and negative prompt
 
@@ -754,13 +751,7 @@ def get_weighted_text_embeddings_sdxl_refiner(
     return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
 
 
-def get_weighted_text_embeddings_sdxl_2p(
-        pipe: StableDiffusionXLPipeline
-        , prompt: str = ""
-        , prompt_2: str = None
-        , neg_prompt: str = ""
-        , neg_prompt_2: str = None
-):
+def get_weighted_text_embeddings_sdxl_2p(pipe: StableDiffusionXLPipeline, prompt: str = "", prompt_2: str | None = None, neg_prompt: str = "", neg_prompt_2: str | None = None):
     """
     This function can process long prompt with weights, no length limitation
     for Stable Diffusion XL, support two prompt sets.
@@ -1345,12 +1336,7 @@ def get_weighted_text_embeddings_sd3(
     return sd3_prompt_embeds, sd3_neg_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
 
 
-def get_weighted_text_embeddings_flux1(
-        pipe: FluxPipeline
-        , prompt: str = ""
-        , prompt2: str = None
-        , device=None
-):
+def get_weighted_text_embeddings_flux1(pipe: FluxPipeline, prompt: str = "", prompt2: str | None = None, device=None):
     """
     This function can process long prompt with weights for flux1 model
 

modules/ras/ras_forward.py

@@ -22,10 +22,10 @@ from . import ras_manager
 def ras_forward(
         self,
         hidden_states: torch.FloatTensor,
-        encoder_hidden_states: torch.FloatTensor = None,
-        pooled_projections: torch.FloatTensor = None,
-        timestep: torch.LongTensor = None,
-        block_controlnet_hidden_states: list = None,
+        encoder_hidden_states: torch.FloatTensor | None = None,
+        pooled_projections: torch.FloatTensor | None = None,
+        timestep: torch.LongTensor | None = None,
+        block_controlnet_hidden_states: list | None = None,
         joint_attention_kwargs: dict[str, Any] | None = None,
         return_dict: bool = True,
         skip_layers: list[int] | None = None,